Tensorflow机器学习入门——MINIST数据集识别(Softmax算法)
参考网站:http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html
import tensorflow as tf def add_layer(inputs, in_size, out_size, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W') biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b') Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases) if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputs #加载数据 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) #构建计算图 x = tf.placeholder("float", [None, 784]) y_ = tf.placeholder("float", [None,10]) y=add_layer(x,784,10,activation_function=tf.nn.softmax) #损失与训练 cross_entropy = -tf.reduce_sum(y_*tf.log(y)) train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy) #计算准确率 correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #训练1000步 init = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init) for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys}) if i%100==0: print (sess.run(accuracy, feed_dict={x: batch_xs, y_: batch_ys})) #验证准确率 print (sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))